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Structurally Sound Dynamic Index Futures Hedging

  • Patrick McGlenchy
  • Paul Kofman
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    Portfolio managers use index futures for a variety of reasons. Regardless of their motivation, they will keep a close eye on the relation between the futures and their stock portfolio returns. Whenever this relation is perceived to have changed, the manager will decide whether it is worthwhile to rebalance the portfolio mix. Exact measures as to when and how much rebalancing should occur, have not yet been established. This paper proposes a heuristic algorithm to dynamically update hedged portfolios. This dynamic hedging algorithm is based on a Reverse Order Cusumsquare (ROC) testing procedure, proposed by Pesaran and Timmermann (2002), to optimally determine forecast estimation windows. In a comparison with standard alternatives (expanding window, EWLS window and rolling window), we find significant improvements in hedging performance, both in- and out-of-samp

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    Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 80.

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    Date of creation: 11 Aug 2004
    Date of revision:
    Handle: RePEc:ecm:ausm04:80
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    1. Ah-Boon Sim & Ralf Zurbruegg, 2001. "Optimal hedge ratios and alternative hedging strategies in the presence of cointegrated time-varying risks," The European Journal of Finance, Taylor & Francis Journals, vol. 7(3), pages 269-283.
    2. Edgerton, David & Wells, Curt, 1994. "Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(3), pages 355-65, August.
    3. Darren Butterworth & Phil Holmes, 2000. ""Ex Ante" Hedging Effectiveness of UK Stock Index Futures Contracts: Evidence for the FTSE 100 and FTSE Mid 250 Contracts," European Financial Management, European Financial Management Association, vol. 6(4), pages 441-457.
    4. Elena Andreou & Eric Ghysels, 2002. "Tests for Breaks in the Conditional Co-movements of Asset Returns," CIRANO Working Papers 2002s-59, CIRANO.
    5. BAI, Jushan & PERRON, Pierre, 1998. "Computation and Analysis of Multiple Structural-Change Models," Cahiers de recherche 9807, Universite de Montreal, Departement de sciences economiques.
    6. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun.
    7. Chris Brooks & Simon Burke & Gita Persand, 2003. "Multivariate GARCH Models: Software Choice and Estimation Issues," ICMA Centre Discussion Papers in Finance icma-dp2003-07, Henley Business School, Reading University.
    8. Pesaran, M. Hashem & Timmermann, Allan, 2002. "Market timing and return prediction under model instability," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 495-510, December.
    9. Perron, P. & Bai, J., 1995. "Estimating and Testing Linear Models with Multiple Structural Changes," Cahiers de recherche 9552, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    10. Cecchetti, Stephen G & Cumby, Robert E & Figlewski, Stephen, 1988. "Estimation of the Optimal Futures Hedge," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 623-30, November.
    11. Lafuente, Juan A. & Novales, Alfonso, 2003. "Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1053-1078, June.
    12. Tong, Wilson H. S., 1996. "An examination of dynamic hedging," Journal of International Money and Finance, Elsevier, vol. 15(1), pages 19-35, February.
    13. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
    14. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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